Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations57259
Missing cells1145
Missing cells (%)0.1%
Duplicate rows3837
Duplicate rows (%)6.7%
Total size in memory14.9 MiB
Average record size in memory273.0 B

Variable types

Categorical14
Numeric15
Text1
Unsupported1
DateTime2

Alerts

Dataset has 3837 (6.7%) duplicate rowsDuplicates
agent is highly overall correlated with hotelHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
children is highly imbalanced (79.8%) Imbalance
meal is highly imbalanced (53.2%) Imbalance
distribution_channel is highly imbalanced (60.3%) Imbalance
is_repeated_guest is highly imbalanced (80.4%) Imbalance
deposit_type is highly imbalanced (70.5%) Imbalance
required_car_parking_spaces is highly imbalanced (80.3%) Imbalance
customer_type has 575 (1.0%) missing values Missing
adults is highly skewed (γ1 = 24.87432688) Skewed
babies is highly skewed (γ1 = 25.35858887) Skewed
previous_cancellations is highly skewed (γ1 = 21.01126487) Skewed
company is an unsupported type, check if it needs cleaning or further analysis Unsupported
lead_time has 3511 (6.1%) zeros Zeros
stays_in_weekend_nights has 22722 (39.7%) zeros Zeros
stays_in_week_nights has 3486 (6.1%) zeros Zeros
babies has 56477 (98.6%) zeros Zeros
previous_cancellations has 56224 (98.2%) zeros Zeros
previous_bookings_not_canceled has 55492 (96.9%) zeros Zeros
booking_changes has 47909 (83.7%) zeros Zeros
agent has 8618 (15.1%) zeros Zeros
days_in_waiting_list has 54921 (95.9%) zeros Zeros
adr has 917 (1.6%) zeros Zeros
total_of_special_requests has 36106 (63.1%) zeros Zeros

Reproduction

Analysis started2025-09-21 17:52:57.063026
Analysis finished2025-09-21 17:53:59.218302
Duration1 minute and 2.16 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

hotel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
Resort Hotel
38783 
City Hotel
18476 

Length

Max length12
Median length12
Mean length11.354652
Min length10

Characters and Unicode

Total characters650156
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
Resort Hotel 38783
67.7%
City Hotel 18476
32.3%

Length

2025-09-21T12:53:59.363876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:53:59.511442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel 57259
50.0%
resort 38783
33.9%
city 18476
 
16.1%

Most occurring characters

ValueCountFrequency (%)
t 114518
17.6%
e 96042
14.8%
o 96042
14.8%
57259
8.8%
H 57259
8.8%
l 57259
8.8%
s 38783
 
6.0%
R 38783
 
6.0%
r 38783
 
6.0%
C 18476
 
2.8%
Other values (2) 36952
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 478379
73.6%
Uppercase Letter 114518
 
17.6%
Space Separator 57259
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 114518
23.9%
e 96042
20.1%
o 96042
20.1%
l 57259
12.0%
s 38783
 
8.1%
r 38783
 
8.1%
i 18476
 
3.9%
y 18476
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
H 57259
50.0%
R 38783
33.9%
C 18476
 
16.1%
Space Separator
ValueCountFrequency (%)
57259
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 592897
91.2%
Common 57259
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 114518
19.3%
e 96042
16.2%
o 96042
16.2%
H 57259
9.7%
l 57259
9.7%
s 38783
 
6.5%
R 38783
 
6.5%
r 38783
 
6.5%
C 18476
 
3.1%
i 18476
 
3.1%
Common
ValueCountFrequency (%)
57259
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 114518
17.6%
e 96042
14.8%
o 96042
14.8%
57259
8.8%
H 57259
8.8%
l 57259
8.8%
s 38783
 
6.0%
R 38783
 
6.0%
r 38783
 
6.0%
C 18476
 
2.8%
Other values (2) 36952
 
5.7%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
0
33535 
1
23724 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters57259
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

Length

2025-09-21T12:53:59.652931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:53:59.754491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

Most occurring characters

ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57259
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

Most occurring scripts

ValueCountFrequency (%)
Common 57259
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33535
58.6%
1 23724
41.4%

lead_time
Real number (ℝ)

Zeros 

Distinct428
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.51677
Minimum0
Maximum737
Zeros3511
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:53:59.902536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median69
Q3158
95-th percentile309
Maximum737
Range737
Interquartile range (IQR)141

Descriptive statistics

Standard deviation101.16703
Coefficient of variation (CV)1.0064691
Kurtosis0.99744677
Mean100.51677
Median Absolute Deviation (MAD)60
Skewness1.204174
Sum5755490
Variance10234.768
MonotonicityNot monotonic
2025-09-21T12:54:00.168000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3511
 
6.1%
1 1827
 
3.2%
2 1051
 
1.8%
3 902
 
1.6%
4 799
 
1.4%
5 715
 
1.2%
7 658
 
1.1%
6 631
 
1.1%
12 510
 
0.9%
10 506
 
0.9%
Other values (418) 46149
80.6%
ValueCountFrequency (%)
0 3511
6.1%
1 1827
3.2%
2 1051
 
1.8%
3 902
 
1.6%
4 799
 
1.4%
5 715
 
1.2%
6 631
 
1.1%
7 658
 
1.1%
8 480
 
0.8%
9 466
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
605 8
 
< 0.1%
542 23
< 0.1%
532 1
 
< 0.1%
471 5
 
< 0.1%
468 46
0.1%
462 19
< 0.1%
461 32
0.1%
460 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
2016
30174 
2015
14255 
2017
12830 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters229036
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 30174
52.7%
2015 14255
24.9%
2017 12830
22.4%

Length

2025-09-21T12:54:00.377085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:00.487139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 30174
52.7%
2015 14255
24.9%
2017 12830
22.4%

Most occurring characters

ValueCountFrequency (%)
2 57259
25.0%
0 57259
25.0%
1 57259
25.0%
6 30174
13.2%
5 14255
 
6.2%
7 12830
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 229036
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 57259
25.0%
0 57259
25.0%
1 57259
25.0%
6 30174
13.2%
5 14255
 
6.2%
7 12830
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 229036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 57259
25.0%
0 57259
25.0%
1 57259
25.0%
6 30174
13.2%
5 14255
 
6.2%
7 12830
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 57259
25.0%
0 57259
25.0%
1 57259
25.0%
6 30174
13.2%
5 14255
 
6.2%
7 12830
 
5.6%

arrival_date_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6900575
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:00.630700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0382557
Coefficient of variation (CV)0.45414493
Kurtosis-0.96336753
Mean6.6900575
Median Absolute Deviation (MAD)2
Skewness-0.14721126
Sum383066
Variance9.2309974
MonotonicityNot monotonic
2025-09-21T12:54:00.888817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 7550
13.2%
9 6555
11.4%
7 6018
10.5%
10 5857
10.2%
5 5157
9.0%
4 5034
8.8%
6 4615
8.1%
3 4365
7.6%
2 3705
6.5%
12 2996
 
5.2%
Other values (2) 5407
9.4%
ValueCountFrequency (%)
1 2657
 
4.6%
2 3705
6.5%
3 4365
7.6%
4 5034
8.8%
5 5157
9.0%
6 4615
8.1%
7 6018
10.5%
8 7550
13.2%
9 6555
11.4%
10 5857
10.2%
ValueCountFrequency (%)
12 2996
 
5.2%
11 2750
 
4.8%
10 5857
10.2%
9 6555
11.4%
8 7550
13.2%
7 6018
10.5%
6 4615
8.1%
5 5157
9.0%
4 5034
8.8%
3 4365
7.6%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.834611
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:01.279260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.311418
Coefficient of variation (CV)0.47823259
Kurtosis-0.93923648
Mean27.834611
Median Absolute Deviation (MAD)11
Skewness-0.13420212
Sum1593782
Variance177.19386
MonotonicityNot monotonic
2025-09-21T12:54:01.602668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 1977
 
3.5%
34 1674
 
2.9%
38 1629
 
2.8%
41 1628
 
2.8%
32 1596
 
2.8%
42 1565
 
2.7%
37 1522
 
2.7%
40 1485
 
2.6%
35 1466
 
2.6%
30 1454
 
2.5%
Other values (43) 41263
72.1%
ValueCountFrequency (%)
1 394
 
0.7%
2 565
1.0%
3 640
1.1%
4 653
1.1%
5 571
1.0%
6 762
1.3%
7 1035
1.8%
8 854
1.5%
9 925
1.6%
10 974
1.7%
ValueCountFrequency (%)
53 780
1.4%
52 632
1.1%
51 437
0.8%
50 489
0.9%
49 811
1.4%
48 686
1.2%
47 786
1.4%
46 524
0.9%
45 722
1.3%
44 962
1.7%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.770307
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:01.792756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7834858
Coefficient of variation (CV)0.55696353
Kurtosis-1.176783
Mean15.770307
Median Absolute Deviation (MAD)8
Skewness0.020637647
Sum902992
Variance77.149622
MonotonicityNot monotonic
2025-09-21T12:54:01.988832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 2184
 
3.8%
12 2174
 
3.8%
16 2151
 
3.8%
17 2063
 
3.6%
18 2057
 
3.6%
30 2051
 
3.6%
26 2048
 
3.6%
9 2034
 
3.6%
25 1968
 
3.4%
15 1953
 
3.4%
Other values (21) 36576
63.9%
ValueCountFrequency (%)
1 1705
3.0%
2 1943
3.4%
3 1790
3.1%
4 1791
3.1%
5 2184
3.8%
6 1728
3.0%
7 1808
3.2%
8 1866
3.3%
9 2034
3.6%
10 1664
2.9%
ValueCountFrequency (%)
31 1156
2.0%
30 2051
3.6%
29 1671
2.9%
28 1757
3.1%
27 1664
2.9%
26 2048
3.6%
25 1968
3.4%
24 1919
3.4%
23 1732
3.0%
22 1762
3.1%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0619466
Minimum0
Maximum16
Zeros22722
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:02.158635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.089239
Coefficient of variation (CV)1.0257004
Kurtosis5.4917112
Mean1.0619466
Median Absolute Deviation (MAD)1
Skewness1.317999
Sum60806
Variance1.1864416
MonotonicityNot monotonic
2025-09-21T12:54:02.329081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 22722
39.7%
2 18024
31.5%
1 13659
23.9%
4 1599
 
2.8%
3 998
 
1.7%
6 125
 
0.2%
5 50
 
0.1%
8 42
 
0.1%
7 17
 
< 0.1%
9 8
 
< 0.1%
Other values (5) 15
 
< 0.1%
ValueCountFrequency (%)
0 22722
39.7%
1 13659
23.9%
2 18024
31.5%
3 998
 
1.7%
4 1599
 
2.8%
5 50
 
0.1%
6 125
 
0.2%
7 17
 
< 0.1%
8 42
 
0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
10 5
 
< 0.1%
9 8
 
< 0.1%
8 42
 
0.1%
7 17
 
< 0.1%
6 125
0.2%
5 50
 
0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8528441
Minimum0
Maximum40
Zeros3486
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:02.526565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2255226
Coefficient of variation (CV)0.78010664
Kurtosis14.450583
Mean2.8528441
Median Absolute Deviation (MAD)1
Skewness2.3788334
Sum163351
Variance4.9529509
MonotonicityNot monotonic
2025-09-21T12:54:02.785868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 14152
24.7%
1 13164
23.0%
3 9420
16.5%
5 8324
14.5%
4 4748
 
8.3%
0 3486
 
6.1%
6 1188
 
2.1%
10 914
 
1.6%
7 864
 
1.5%
8 515
 
0.9%
Other values (21) 484
 
0.8%
ValueCountFrequency (%)
0 3486
 
6.1%
1 13164
23.0%
2 14152
24.7%
3 9420
16.5%
4 4748
 
8.3%
5 8324
14.5%
6 1188
 
2.1%
7 864
 
1.5%
8 515
 
0.9%
9 176
 
0.3%
ValueCountFrequency (%)
40 2
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 4
 
< 0.1%
26 1
 
< 0.1%
25 5
< 0.1%
24 1
 
< 0.1%
22 2
 
< 0.1%
21 11
< 0.1%

adults
Real number (ℝ)

Skewed 

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9731221
Minimum-1
Maximum100
Zeros98
Zeros (%)0.2%
Negative92
Negative (%)0.2%
Memory size894.7 KiB
2025-09-21T12:54:03.018994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum100
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9399952
Coefficient of variation (CV)1.4900219
Kurtosis661.32331
Mean1.9731221
Median Absolute Deviation (MAD)0
Skewness24.874327
Sum112979
Variance8.6435718
MonotonicityNot monotonic
2025-09-21T12:54:03.256591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 44642
78.0%
1 10109
 
17.7%
3 2176
 
3.8%
0 98
 
0.2%
-1 92
 
0.2%
4 34
 
0.1%
66 6
 
< 0.1%
26 5
 
< 0.1%
65 5
 
< 0.1%
53 4
 
< 0.1%
Other values (44) 88
 
0.2%
ValueCountFrequency (%)
-1 92
 
0.2%
0 98
 
0.2%
1 10109
 
17.7%
2 44642
78.0%
3 2176
 
3.8%
4 34
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
100 3
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
95 3
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 4
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
86 2
< 0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
0
52882 
1
 
2281
2
 
2069
3
 
26
10
 
1

Length

Max length2
Median length1
Mean length1.0000175
Min length1

Characters and Unicode

Total characters57260
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 52882
92.4%
1 2281
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%
10 1
 
< 0.1%

Length

2025-09-21T12:54:03.474684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:03.735147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 52882
92.4%
1 2281
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 52883
92.4%
1 2282
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57260
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52883
92.4%
1 2282
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 57260
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52883
92.4%
1 2282
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52883
92.4%
1 2282
 
4.0%
2 2069
 
3.6%
3 26
 
< 0.1%

babies
Real number (ℝ)

Skewed  Zeros 

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13699156
Minimum-1
Maximum100
Zeros56477
Zeros (%)98.6%
Negative90
Negative (%)0.2%
Memory size894.7 KiB
2025-09-21T12:54:04.170592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1286788
Coefficient of variation (CV)22.838478
Kurtosis665.52972
Mean0.13699156
Median Absolute Deviation (MAD)0
Skewness25.358589
Sum7844
Variance9.7886311
MonotonicityNot monotonic
2025-09-21T12:54:04.684023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 56477
98.6%
1 584
 
1.0%
-1 90
 
0.2%
2 7
 
< 0.1%
57 5
 
< 0.1%
51 5
 
< 0.1%
73 5
 
< 0.1%
77 5
 
< 0.1%
94 4
 
< 0.1%
97 4
 
< 0.1%
Other values (37) 73
 
0.1%
ValueCountFrequency (%)
-1 90
 
0.2%
0 56477
98.6%
1 584
 
1.0%
2 7
 
< 0.1%
10 1
 
< 0.1%
50 1
 
< 0.1%
51 5
 
< 0.1%
52 2
 
< 0.1%
53 2
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
100 2
< 0.1%
99 2
< 0.1%
98 2
< 0.1%
97 4
< 0.1%
96 2
< 0.1%
95 1
 
< 0.1%
94 4
< 0.1%
93 3
< 0.1%
92 3
< 0.1%
91 1
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
BB
43715 
HB
9870 
SC
 
1747
Undefined
 
1147
FB
 
780

Length

Max length9
Median length2
Mean length2.1402225
Min length2

Characters and Unicode

Total characters122547
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 43715
76.3%
HB 9870
 
17.2%
SC 1747
 
3.1%
Undefined 1147
 
2.0%
FB 780
 
1.4%

Length

2025-09-21T12:54:05.069888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:05.286975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bb 43715
76.3%
hb 9870
 
17.2%
sc 1747
 
3.1%
undefined 1147
 
2.0%
fb 780
 
1.4%

Most occurring characters

ValueCountFrequency (%)
B 98080
80.0%
H 9870
 
8.1%
d 2294
 
1.9%
e 2294
 
1.9%
n 2294
 
1.9%
S 1747
 
1.4%
C 1747
 
1.4%
U 1147
 
0.9%
f 1147
 
0.9%
i 1147
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 113371
92.5%
Lowercase Letter 9176
 
7.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 98080
86.5%
H 9870
 
8.7%
S 1747
 
1.5%
C 1747
 
1.5%
U 1147
 
1.0%
F 780
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
d 2294
25.0%
e 2294
25.0%
n 2294
25.0%
f 1147
12.5%
i 1147
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 122547
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 98080
80.0%
H 9870
 
8.1%
d 2294
 
1.9%
e 2294
 
1.9%
n 2294
 
1.9%
S 1747
 
1.4%
C 1747
 
1.4%
U 1147
 
0.9%
f 1147
 
0.9%
i 1147
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 98080
80.0%
H 9870
 
8.1%
d 2294
 
1.9%
e 2294
 
1.9%
n 2294
 
1.9%
S 1747
 
1.4%
C 1747
 
1.4%
U 1147
 
0.9%
f 1147
 
0.9%
i 1147
 
0.9%
Distinct141
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:05.823890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9863078
Min length2

Characters and Unicode

Total characters170993
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 27023
47.2%
gbr 7424
 
13.0%
esp 5177
 
9.0%
fra 2980
 
5.2%
irl 2325
 
4.1%
deu 1981
 
3.5%
ita 1261
 
2.2%
cn 784
 
1.4%
nld 732
 
1.3%
bel 721
 
1.3%
Other values (131) 6851
 
12.0%
2025-09-21T12:54:06.528292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 41955
24.5%
P 32772
19.2%
T 28965
16.9%
E 9124
 
5.3%
B 8954
 
5.2%
G 7810
 
4.6%
S 7081
 
4.1%
A 6771
 
4.0%
L 4605
 
2.7%
U 4184
 
2.4%
Other values (16) 18772
11.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 170993
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 41955
24.5%
P 32772
19.2%
T 28965
16.9%
E 9124
 
5.3%
B 8954
 
5.2%
G 7810
 
4.6%
S 7081
 
4.1%
A 6771
 
4.0%
L 4605
 
2.7%
U 4184
 
2.4%
Other values (16) 18772
11.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170993
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 41955
24.5%
P 32772
19.2%
T 28965
16.9%
E 9124
 
5.3%
B 8954
 
5.2%
G 7810
 
4.6%
S 7081
 
4.1%
A 6771
 
4.0%
L 4605
 
2.7%
U 4184
 
2.4%
Other values (16) 18772
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 41955
24.5%
P 32772
19.2%
T 28965
16.9%
E 9124
 
5.3%
B 8954
 
5.2%
G 7810
 
4.6%
S 7081
 
4.1%
A 6771
 
4.0%
L 4605
 
2.7%
U 4184
 
2.4%
Other values (16) 18772
11.0%

market_segment
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
Online TA
25190 
Offline TA/TO
12148 
Groups
10179 
Direct
7093 
Corporate
 
2381
Other values (3)
 
268

Length

Max length13
Median length9
Mean length8.9604429
Min length6

Characters and Unicode

Total characters513066
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 25190
44.0%
Offline TA/TO 12148
21.2%
Groups 10179
17.8%
Direct 7093
 
12.4%
Corporate 2381
 
4.2%
Complementary 245
 
0.4%
Aviation 21
 
< 0.1%
Undefined 2
 
< 0.1%

Length

2025-09-21T12:54:06.868243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:07.352681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online 25190
26.6%
ta 25190
26.6%
offline 12148
12.8%
ta/to 12148
12.8%
groups 10179
10.8%
direct 7093
 
7.5%
corporate 2381
 
2.5%
complementary 245
 
0.3%
aviation 21
 
< 0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 62798
12.2%
O 49486
9.6%
T 49486
9.6%
e 47306
9.2%
i 44475
8.7%
l 37583
 
7.3%
A 37359
 
7.3%
37338
 
7.3%
f 24298
 
4.7%
r 22279
 
4.3%
Other values (16) 100658
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 307349
59.9%
Uppercase Letter 156231
30.5%
Space Separator 37338
 
7.3%
Other Punctuation 12148
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 62798
20.4%
e 47306
15.4%
i 44475
14.5%
l 37583
12.2%
f 24298
 
7.9%
r 22279
 
7.2%
o 15207
 
4.9%
p 12805
 
4.2%
u 10179
 
3.3%
s 10179
 
3.3%
Other values (7) 20240
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
O 49486
31.7%
T 49486
31.7%
A 37359
23.9%
G 10179
 
6.5%
D 7093
 
4.5%
C 2626
 
1.7%
U 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
37338
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 12148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 463580
90.4%
Common 49486
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 62798
13.5%
O 49486
10.7%
T 49486
10.7%
e 47306
10.2%
i 44475
9.6%
l 37583
8.1%
A 37359
8.1%
f 24298
 
5.2%
r 22279
 
4.8%
o 15207
 
3.3%
Other values (14) 73303
15.8%
Common
ValueCountFrequency (%)
37338
75.5%
/ 12148
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 62798
12.2%
O 49486
9.6%
T 49486
9.6%
e 47306
9.2%
i 44475
8.7%
l 37583
 
7.3%
A 37359
 
7.3%
37338
 
7.3%
f 24298
 
4.7%
r 22279
 
4.3%
Other values (16) 100658
19.6%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
TA/TO
45327 
Direct
8504 
Corporate
 
3412
GDS
 
11
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3868388
Min length3

Characters and Unicode

Total characters308445
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 45327
79.2%
Direct 8504
 
14.9%
Corporate 3412
 
6.0%
GDS 11
 
< 0.1%
Undefined 5
 
< 0.1%

Length

2025-09-21T12:54:07.879624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:08.207954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 45327
79.2%
direct 8504
 
14.9%
corporate 3412
 
6.0%
gds 11
 
< 0.1%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 90654
29.4%
A 45327
14.7%
/ 45327
14.7%
O 45327
14.7%
r 15328
 
5.0%
e 11926
 
3.9%
t 11916
 
3.9%
D 8515
 
2.8%
i 8509
 
2.8%
c 8504
 
2.8%
Other values (10) 17112
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 193262
62.7%
Lowercase Letter 69856
 
22.6%
Other Punctuation 45327
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 15328
21.9%
e 11926
17.1%
t 11916
17.1%
i 8509
12.2%
c 8504
12.2%
o 6824
9.8%
p 3412
 
4.9%
a 3412
 
4.9%
n 10
 
< 0.1%
d 10
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 90654
46.9%
A 45327
23.5%
O 45327
23.5%
D 8515
 
4.4%
C 3412
 
1.8%
G 11
 
< 0.1%
S 11
 
< 0.1%
U 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 45327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 263118
85.3%
Common 45327
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 90654
34.5%
A 45327
17.2%
O 45327
17.2%
r 15328
 
5.8%
e 11926
 
4.5%
t 11916
 
4.5%
D 8515
 
3.2%
i 8509
 
3.2%
c 8504
 
3.2%
o 6824
 
2.6%
Other values (9) 10288
 
3.9%
Common
ValueCountFrequency (%)
/ 45327
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 90654
29.4%
A 45327
14.7%
/ 45327
14.7%
O 45327
14.7%
r 15328
 
5.0%
e 11926
 
3.9%
t 11916
 
3.9%
D 8515
 
2.8%
i 8509
 
2.8%
c 8504
 
2.8%
Other values (10) 17112
 
5.5%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
0
55525 
1
 
1734

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters57259
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

Length

2025-09-21T12:54:08.481497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:08.605053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57259
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57259
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55525
97.0%
1 1734
 
3.0%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06945633
Minimum0
Maximum26
Zeros56224
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:08.742101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1127908
Coefficient of variation (CV)16.021445
Kurtosis452.18405
Mean0.06945633
Median Absolute Deviation (MAD)0
Skewness21.011265
Sum3977
Variance1.2383033
MonotonicityNot monotonic
2025-09-21T12:54:08.908194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 56224
98.2%
1 843
 
1.5%
24 48
 
0.1%
2 40
 
0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
19 18
 
< 0.1%
3 14
 
< 0.1%
14 13
 
< 0.1%
4 5
 
< 0.1%
ValueCountFrequency (%)
0 56224
98.2%
1 843
 
1.5%
2 40
 
0.1%
3 14
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
14 13
 
< 0.1%
19 18
 
< 0.1%
24 48
 
0.1%
25 25
 
< 0.1%
ValueCountFrequency (%)
26 26
 
< 0.1%
25 25
 
< 0.1%
24 48
 
0.1%
19 18
 
< 0.1%
14 13
 
< 0.1%
5 3
 
< 0.1%
4 5
 
< 0.1%
3 14
 
< 0.1%
2 40
 
0.1%
1 843
1.5%

previous_bookings_not_canceled
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08737491
Minimum0
Maximum30
Zeros55492
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:09.121277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76767839
Coefficient of variation (CV)8.7860278
Kurtosis407.53657
Mean0.08737491
Median Absolute Deviation (MAD)0
Skewness17.018519
Sum5003
Variance0.58933011
MonotonicityNot monotonic
2025-09-21T12:54:09.329380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 55492
96.9%
1 864
 
1.5%
2 336
 
0.6%
3 169
 
0.3%
4 107
 
0.2%
5 78
 
0.1%
6 50
 
0.1%
7 31
 
0.1%
8 30
 
0.1%
9 19
 
< 0.1%
Other values (20) 83
 
0.1%
ValueCountFrequency (%)
0 55492
96.9%
1 864
 
1.5%
2 336
 
0.6%
3 169
 
0.3%
4 107
 
0.2%
5 78
 
0.1%
6 50
 
0.1%
7 31
 
0.1%
8 30
 
0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
24 2
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 2
< 0.1%
20 1
 
< 0.1%

reserved_room_type
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
A
37981 
D
9312 
E
5004 
G
 
1601
F
 
1477
Other values (4)
 
1884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters57259
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 37981
66.3%
D 9312
 
16.3%
E 5004
 
8.7%
G 1601
 
2.8%
F 1477
 
2.6%
C 898
 
1.6%
H 589
 
1.0%
B 392
 
0.7%
L 5
 
< 0.1%

Length

2025-09-21T12:54:09.587984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:09.758055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 37981
66.3%
d 9312
 
16.3%
e 5004
 
8.7%
g 1601
 
2.8%
f 1477
 
2.6%
c 898
 
1.6%
h 589
 
1.0%
b 392
 
0.7%
l 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 37981
66.3%
D 9312
 
16.3%
E 5004
 
8.7%
G 1601
 
2.8%
F 1477
 
2.6%
C 898
 
1.6%
H 589
 
1.0%
B 392
 
0.7%
L 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57259
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 37981
66.3%
D 9312
 
16.3%
E 5004
 
8.7%
G 1601
 
2.8%
F 1477
 
2.6%
C 898
 
1.6%
H 589
 
1.0%
B 392
 
0.7%
L 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 57259
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 37981
66.3%
D 9312
 
16.3%
E 5004
 
8.7%
G 1601
 
2.8%
F 1477
 
2.6%
C 898
 
1.6%
H 589
 
1.0%
B 392
 
0.7%
L 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 37981
66.3%
D 9312
 
16.3%
E 5004
 
8.7%
G 1601
 
2.8%
F 1477
 
2.6%
C 898
 
1.6%
H 589
 
1.0%
B 392
 
0.7%
L 5
 
< 0.1%

assigned_room_type
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
A
30596 
D
12924 
E
5739 
C
 
2155
F
 
2114
Other values (5)
3731 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters57259
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 30596
53.4%
D 12924
22.6%
E 5739
 
10.0%
C 2155
 
3.8%
F 2114
 
3.7%
G 1863
 
3.3%
B 805
 
1.4%
H 693
 
1.2%
I 348
 
0.6%
K 22
 
< 0.1%

Length

2025-09-21T12:54:09.990148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:10.172880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 30596
53.4%
d 12924
22.6%
e 5739
 
10.0%
c 2155
 
3.8%
f 2114
 
3.7%
g 1863
 
3.3%
b 805
 
1.4%
h 693
 
1.2%
i 348
 
0.6%
k 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 30596
53.4%
D 12924
22.6%
E 5739
 
10.0%
C 2155
 
3.8%
F 2114
 
3.7%
G 1863
 
3.3%
B 805
 
1.4%
H 693
 
1.2%
I 348
 
0.6%
K 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57259
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 30596
53.4%
D 12924
22.6%
E 5739
 
10.0%
C 2155
 
3.8%
F 2114
 
3.7%
G 1863
 
3.3%
B 805
 
1.4%
H 693
 
1.2%
I 348
 
0.6%
K 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 57259
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 30596
53.4%
D 12924
22.6%
E 5739
 
10.0%
C 2155
 
3.8%
F 2114
 
3.7%
G 1863
 
3.3%
B 805
 
1.4%
H 693
 
1.2%
I 348
 
0.6%
K 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 30596
53.4%
D 12924
22.6%
E 5739
 
10.0%
C 2155
 
3.8%
F 2114
 
3.7%
G 1863
 
3.3%
B 805
 
1.4%
H 693
 
1.2%
I 348
 
0.6%
K 22
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24287885
Minimum0
Maximum20
Zeros47909
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:10.371963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69558072
Coefficient of variation (CV)2.8639
Kurtosis69.801833
Mean0.24287885
Median Absolute Deviation (MAD)0
Skewness5.7791198
Sum13907
Variance0.48383254
MonotonicityNot monotonic
2025-09-21T12:54:10.555129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 47909
83.7%
1 6555
 
11.4%
2 1872
 
3.3%
3 537
 
0.9%
4 214
 
0.4%
5 76
 
0.1%
6 42
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
Other values (8) 15
 
< 0.1%
ValueCountFrequency (%)
0 47909
83.7%
1 6555
 
11.4%
2 1872
 
3.3%
3 537
 
0.9%
4 214
 
0.4%
5 76
 
0.1%
6 42
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
10 2
 
< 0.1%
9 7
< 0.1%
8 11
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
No Deposit
50844 
Non Refund
5333 
No Refund
 
941
Refundable
 
141

Length

Max length10
Median length10
Mean length9.9835659
Min length9

Characters and Unicode

Total characters571649
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 50844
88.8%
Non Refund 5333
 
9.3%
No Refund 941
 
1.6%
Refundable 141
 
0.2%

Length

2025-09-21T12:54:10.894135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:11.123387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 51785
45.3%
deposit 50844
44.5%
refund 6274
 
5.5%
non 5333
 
4.7%
refundable 141
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 107962
18.9%
e 57400
10.0%
N 57118
10.0%
57118
10.0%
D 50844
8.9%
p 50844
8.9%
s 50844
8.9%
i 50844
8.9%
t 50844
8.9%
n 11748
 
2.1%
Other values (7) 26083
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400154
70.0%
Uppercase Letter 114377
 
20.0%
Space Separator 57118
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 107962
27.0%
e 57400
14.3%
p 50844
12.7%
s 50844
12.7%
i 50844
12.7%
t 50844
12.7%
n 11748
 
2.9%
f 6415
 
1.6%
u 6415
 
1.6%
d 6415
 
1.6%
Other values (3) 423
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 57118
49.9%
D 50844
44.5%
R 6415
 
5.6%
Space Separator
ValueCountFrequency (%)
57118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 514531
90.0%
Common 57118
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 107962
21.0%
e 57400
11.2%
N 57118
11.1%
D 50844
9.9%
p 50844
9.9%
s 50844
9.9%
i 50844
9.9%
t 50844
9.9%
n 11748
 
2.3%
R 6415
 
1.2%
Other values (6) 19668
 
3.8%
Common
ValueCountFrequency (%)
57118
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 571649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 107962
18.9%
e 57400
10.0%
N 57118
10.0%
57118
10.0%
D 50844
8.9%
p 50844
8.9%
s 50844
8.9%
i 50844
8.9%
t 50844
8.9%
n 11748
 
2.1%
Other values (7) 26083
 
4.6%

agent
Real number (ℝ)

High correlation  Zeros 

Distinct249
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.64032
Minimum0
Maximum535
Zeros8618
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:11.412922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median75
Q3240
95-th percentile298
Maximum535
Range535
Interquartile range (IQR)233

Descriptive statistics

Standard deviation122.52477
Coefficient of variation (CV)0.98302677
Kurtosis-1.1083975
Mean124.64032
Median Absolute Deviation (MAD)75
Skewness0.38339404
Sum7136780
Variance15012.319
MonotonicityNot monotonic
2025-09-21T12:54:11.657534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 13579
23.7%
0 8618
15.1%
9 6891
12.0%
1 3119
 
5.4%
250 2791
 
4.9%
241 1677
 
2.9%
6 1349
 
2.4%
40 990
 
1.7%
314 904
 
1.6%
242 762
 
1.3%
Other values (239) 16579
29.0%
ValueCountFrequency (%)
0 8618
15.1%
1 3119
 
5.4%
2 117
 
0.2%
3 545
 
1.0%
5 248
 
0.4%
6 1349
 
2.4%
7 476
 
0.8%
8 549
 
1.0%
9 6891
12.0%
10 38
 
0.1%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 65
0.1%
527 35
0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
508 6
 
< 0.1%
502 23
 
< 0.1%
497 1
 
< 0.1%
495 50
0.1%
493 34
0.1%

company
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size894.7 KiB

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5441939
Minimum0
Maximum391
Zeros54921
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:11.899615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.887563
Coefficient of variation (CV)6.1756109
Kurtosis100.35843
Mean3.5441939
Median Absolute Deviation (MAD)0
Skewness8.8782447
Sum202937
Variance479.0654
MonotonicityNot monotonic
2025-09-21T12:54:12.170754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54921
95.9%
39 180
 
0.3%
58 163
 
0.3%
31 98
 
0.2%
69 89
 
0.2%
87 78
 
0.1%
63 77
 
0.1%
111 68
 
0.1%
101 63
 
0.1%
77 62
 
0.1%
Other values (89) 1460
 
2.5%
ValueCountFrequency (%)
0 54921
95.9%
1 7
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.1%
4 10
 
< 0.1%
5 4
 
< 0.1%
6 4
 
< 0.1%
8 6
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
391 14
 
< 0.1%
379 15
 
< 0.1%
330 14
 
< 0.1%
259 10
 
< 0.1%
236 35
0.1%
224 10
 
< 0.1%
223 59
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
187 45
0.1%

customer_type
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing575
Missing (%)1.0%
Memory size894.7 KiB
Transient
41176 
Transient-Party
12766 
Contract
 
2442
Group
 
300

Length

Max length15
Median length9
Mean length10.28703
Min length5

Characters and Unicode

Total characters583110
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 41176
71.9%
Transient-Party 12766
 
22.3%
Contract 2442
 
4.3%
Group 300
 
0.5%
(Missing) 575
 
1.0%

Length

2025-09-21T12:54:12.358976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:12.994906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transient 41176
72.6%
transient-party 12766
 
22.5%
contract 2442
 
4.3%
group 300
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 110326
18.9%
t 71592
12.3%
r 69450
11.9%
a 69150
11.9%
T 53942
9.3%
s 53942
9.3%
i 53942
9.3%
e 53942
9.3%
- 12766
 
2.2%
P 12766
 
2.2%
Other values (7) 21292
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 500894
85.9%
Uppercase Letter 69450
 
11.9%
Dash Punctuation 12766
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 110326
22.0%
t 71592
14.3%
r 69450
13.9%
a 69150
13.8%
s 53942
10.8%
i 53942
10.8%
e 53942
10.8%
y 12766
 
2.5%
o 2742
 
0.5%
c 2442
 
0.5%
Other values (2) 600
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 53942
77.7%
P 12766
 
18.4%
C 2442
 
3.5%
G 300
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 12766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 570344
97.8%
Common 12766
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 110326
19.3%
t 71592
12.6%
r 69450
12.2%
a 69150
12.1%
T 53942
9.5%
s 53942
9.5%
i 53942
9.5%
e 53942
9.5%
P 12766
 
2.2%
y 12766
 
2.2%
Other values (6) 8526
 
1.5%
Common
ValueCountFrequency (%)
- 12766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 583110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 110326
18.9%
t 71592
12.3%
r 69450
11.9%
a 69150
11.9%
T 53942
9.3%
s 53942
9.3%
i 53942
9.3%
e 53942
9.3%
- 12766
 
2.2%
P 12766
 
2.2%
Other values (7) 21292
 
3.7%

adr
Real number (ℝ)

Zeros 

Distinct6680
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.599672
Minimum-6.38
Maximum5400
Zeros917
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size894.7 KiB
2025-09-21T12:54:13.199988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile34.427
Q160
median84.7
Q3120.44
95-th percentile207.9
Maximum5400
Range5406.38
Interquartile range (IQR)60.44

Descriptive statistics

Standard deviation58.668984
Coefficient of variation (CV)0.60734144
Kurtosis1166.914
Mean96.599672
Median Absolute Deviation (MAD)29.3
Skewness13.877957
Sum5531200.6
Variance3442.0497
MonotonicityNot monotonic
2025-09-21T12:54:13.435592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 1756
 
3.1%
75 1055
 
1.8%
48 991
 
1.7%
0 917
 
1.6%
65 902
 
1.6%
60 779
 
1.4%
90 714
 
1.2%
120 688
 
1.2%
80 680
 
1.2%
70 647
 
1.1%
Other values (6670) 48130
84.1%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 917
1.6%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 3
 
< 0.1%
1.56 2
 
< 0.1%
1.8 1
 
< 0.1%
2 8
 
< 0.1%
2.4 1
 
< 0.1%
4 17
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
508 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
388 2
< 0.1%
387 1
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing570
Missing (%)1.0%
Memory size894.7 KiB
0.0
51303 
1.0
5359 
2.0
 
24
8.0
 
2
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters170067
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 51303
89.6%
1.0 5359
 
9.4%
2.0 24
 
< 0.1%
8.0 2
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 570
 
1.0%

Length

2025-09-21T12:54:13.789488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:14.046926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 51303
90.5%
1.0 5359
 
9.5%
2.0 24
 
< 0.1%
8.0 2
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 107992
63.5%
. 56689
33.3%
1 5359
 
3.2%
2 24
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113378
66.7%
Other Punctuation 56689
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 107992
95.2%
1 5359
 
4.7%
2 24
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 56689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 170067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 107992
63.5%
. 56689
33.3%
1 5359
 
3.2%
2 24
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 107992
63.5%
. 56689
33.3%
1 5359
 
3.2%
2 24
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51258317
Minimum0
Maximum5
Zeros36106
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size894.7 KiB
2025-09-21T12:54:14.241566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76801752
Coefficient of variation (CV)1.4983276
Kurtosis1.8446116
Mean0.51258317
Median Absolute Deviation (MAD)0
Skewness1.4828921
Sum29350
Variance0.58985092
MonotonicityNot monotonic
2025-09-21T12:54:14.451669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 36106
63.1%
1 14315
 
25.0%
2 5646
 
9.9%
3 1036
 
1.8%
4 145
 
0.3%
5 11
 
< 0.1%
ValueCountFrequency (%)
0 36106
63.1%
1 14315
 
25.0%
2 5646
 
9.9%
3 1036
 
1.8%
4 145
 
0.3%
5 11
 
< 0.1%
ValueCountFrequency (%)
5 11
 
< 0.1%
4 145
 
0.3%
3 1036
 
1.8%
2 5646
 
9.9%
1 14315
 
25.0%
0 36106
63.1%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
Check-Out
33535 
Canceled
22936 
No-Show
 
788

Length

Max length9
Median length9
Mean length8.5719101
Min length7

Characters and Unicode

Total characters490819
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 33535
58.6%
Canceled 22936
40.1%
No-Show 788
 
1.4%

Length

2025-09-21T12:54:14.687263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T12:54:14.858346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out 33535
58.6%
canceled 22936
40.1%
no-show 788
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 79407
16.2%
C 56471
11.5%
c 56471
11.5%
h 34323
7.0%
- 34323
7.0%
k 33535
6.8%
O 33535
6.8%
u 33535
6.8%
t 33535
6.8%
a 22936
 
4.7%
Other values (7) 72748
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 364914
74.3%
Uppercase Letter 91582
 
18.7%
Dash Punctuation 34323
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 79407
21.8%
c 56471
15.5%
h 34323
9.4%
k 33535
9.2%
u 33535
9.2%
t 33535
9.2%
a 22936
 
6.3%
n 22936
 
6.3%
l 22936
 
6.3%
d 22936
 
6.3%
Other values (2) 2364
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 56471
61.7%
O 33535
36.6%
N 788
 
0.9%
S 788
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 34323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 456496
93.0%
Common 34323
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 79407
17.4%
C 56471
12.4%
c 56471
12.4%
h 34323
7.5%
k 33535
7.3%
O 33535
7.3%
u 33535
7.3%
t 33535
7.3%
a 22936
 
5.0%
n 22936
 
5.0%
Other values (6) 49812
10.9%
Common
ValueCountFrequency (%)
- 34323
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 490819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 79407
16.2%
C 56471
11.5%
c 56471
11.5%
h 34323
7.0%
- 34323
7.0%
k 33535
6.8%
O 33535
6.8%
u 33535
6.8%
t 33535
6.8%
a 22936
 
4.7%
Other values (7) 72748
14.8%
Distinct921
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
Minimum2014-11-18 00:00:00
Maximum2017-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-21T12:54:15.073436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:54:15.327033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct793
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
Minimum2015-07-01 00:00:00
Maximum2017-08-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-21T12:54:15.554624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:54:15.820249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-09-21T12:53:54.722950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:11.167815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:15.203047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:18.377426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:21.663348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.374145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.156050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:29.670247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:32.823264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:35.854320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.233881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:42.301760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:45.465283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.565015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:51.930268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.857242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:11.370922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:15.473188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:18.583515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:21.844923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.529761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.303207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:29.866333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:32.999115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.057407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.413790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:42.498885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:45.634794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.742570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:52.068950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.997730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:11.603014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:15.648287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:18.748794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:21.988605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.697838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.467327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:30.042546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:33.171197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.225985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.583369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:42.657265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:45.862643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.923668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:52.229522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:55.272706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:11.946702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:15.817379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:18.927878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.163716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.838806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.614954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:30.217547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:33.344755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.400554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.740970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:42.820828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:46.034215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:49.120771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:52.512030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:55.594395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:12.282248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.007385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:19.108956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.312322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.011001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.750024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:30.405747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:33.516840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.571642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.903627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:43.019462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:46.191940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:49.327868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:52.816056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:55.863210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:12.592408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.170929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:19.303029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.462990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.159168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.884587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:30.617047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:33.710197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.756731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:40.070708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:43.202008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:46.453098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:49.668092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:53.044145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.030809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:12.854531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.366401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:19.485587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.633610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.303318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.015455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:30.825054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:33.885778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:36.947022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:40.240271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:43.399964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:46.793965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:50.031534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:53.212798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.171895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:13.063632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.522964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:19.664671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.781203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.435947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.138497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:31.008503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:34.051849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:37.121108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:40.396838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:43.698280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:47.108286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:50.289024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:53.672593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.321471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:13.316256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.668674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:19.890761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:22.935102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.571592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.277559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:31.176588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:34.202912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:37.282670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:40.660044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:44.031560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:47.342200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:50.507643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:53.784201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.464035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:13.547281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.820989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:20.133438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:23.059731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.701229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.410203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:31.333141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:34.373003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:37.433055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:41.004649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:44.348280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:47.541287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:50.692211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:53.894232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.612100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:13.758897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:16.978534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:20.330503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:23.189465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.841318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.537343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:31.508709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:34.553839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:37.657112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:41.316319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:44.537319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:47.712370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:50.933841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.017849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.752821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:14.047223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:17.154114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:20.612791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:23.468275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:25.989850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.677932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:31.737323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:34.847830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:37.986575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:41.552933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:44.755941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:47.887947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:51.225982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.160457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:56.879471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:14.259844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:17.504464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:20.958041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:23.780524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:26.168586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:28.805984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:32.050818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:35.167030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:38.710668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:41.731494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:44.909569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.062796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:51.402527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.307044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:57.032091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:14.484458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:17.828634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:21.250036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.066915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:26.506139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:29.102676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:32.389179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:35.472133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:38.900229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:41.937100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:45.115650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.240353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:51.595607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.453825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:57.159703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:14.832561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:18.124814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:21.459258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:24.227502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:27.006831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:29.424017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:32.640789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:35.689231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:39.065312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:42.111181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:45.273405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:48.405448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:51.778712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-21T12:53:54.590942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-21T12:54:16.073883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.3030.1120.0320.1100.1110.0000.0000.036-0.0090.0000.0000.0230.0110.0000.0000.0000.0000.0890.0000.000-0.134-0.0820.0000.0000.0000.1460.0940.130
adults0.3031.0000.1240.0050.0430.0420.0160.0000.030-0.0490.0000.106-0.0340.0000.0030.0090.0130.0000.1680.0070.000-0.200-0.0220.0000.0060.0000.1450.1300.138
agent0.1120.1241.000-0.012-0.042-0.0460.2270.1170.0240.0260.0800.179-0.0620.1430.1420.6160.1650.0590.0880.2630.186-0.123-0.0220.0810.1210.1280.2130.2150.246
arrival_date_day_of_month0.0320.005-0.0121.000-0.0380.0530.0460.0150.0000.0060.0120.0340.0300.0610.0380.0500.0200.016-0.0060.0420.0500.009-0.0240.0100.0220.016-0.018-0.0160.005
arrival_date_month0.1100.043-0.042-0.0381.0000.9950.4220.0460.009-0.0020.0770.1240.0230.1230.0850.2340.1680.1230.1100.1110.102-0.0700.0520.0300.1320.0600.0260.0200.018
arrival_date_week_number0.1110.042-0.0460.0530.9951.0000.4190.0470.008-0.0020.0700.1180.0280.1180.0920.2480.1820.1280.1090.1040.101-0.0700.0480.0300.1420.0560.0230.0190.015
arrival_date_year0.0000.0160.2270.0460.4220.4191.0000.1190.0000.0310.0570.1570.0830.1170.0500.3720.2150.1010.1390.1330.1260.0450.0580.0420.1520.1410.0950.0720.122
assigned_room_type0.0000.0000.1170.0150.0460.0470.1191.0000.0000.0810.3270.0970.0430.1690.1060.3820.2620.0860.0610.1320.0970.0110.0190.0960.1880.7320.0710.0620.085
babies0.0360.0300.0240.0000.0090.0080.0000.0001.0000.1050.0000.000-0.0190.0080.0000.0000.0000.000-0.0070.0000.000-0.016-0.0100.0230.0000.0000.0310.0260.100
booking_changes-0.009-0.0490.0260.006-0.002-0.0020.0310.0810.1051.0000.0220.035-0.0240.0220.0300.0410.0570.0000.0280.0230.0180.025-0.0300.0180.0400.0160.0950.0580.055
children0.0000.0000.0800.0120.0770.0700.0570.3270.0000.0221.0000.0620.0220.0580.0410.0610.0430.0330.0250.1050.0300.0000.0000.0310.0390.3860.0300.0330.049
customer_type0.0000.1060.1790.0340.1240.1180.1570.0970.0000.0350.0621.0000.1060.1150.0980.1090.1990.1530.0750.3110.1260.0360.0060.0570.1410.1220.1490.1320.115
days_in_waiting_list0.023-0.034-0.0620.0300.0230.0280.0830.043-0.019-0.0240.0220.1061.0000.1130.0340.2210.0610.0270.1830.0920.063-0.033-0.0270.0420.0470.038-0.005-0.100-0.138
deposit_type0.0110.0000.1430.0610.1230.1180.1170.1690.0080.0220.0580.1150.1131.0000.0740.3070.4100.0610.2270.2750.0690.0120.0630.0680.2970.1270.0750.0650.155
distribution_channel0.0000.0030.1420.0380.0850.0920.0500.1060.0000.0300.0410.0980.0340.0741.0000.2300.1990.2180.1130.6650.0630.1030.0370.0790.1450.1190.0410.0690.077
hotel0.0000.0090.6160.0500.2340.2480.3720.3820.0000.0410.0610.1090.2210.3070.2301.0000.3950.1220.1520.2190.2860.0560.0350.2030.3960.3180.2690.1760.222
is_canceled0.0000.0130.1650.0200.1680.1820.2150.2620.0000.0570.0430.1990.0610.4100.1990.3951.0000.1260.2390.2280.1410.0640.0570.2721.0000.0910.0680.0430.218
is_repeated_guest0.0000.0000.0590.0160.1230.1280.1010.0860.0000.0000.0330.1530.0270.0610.2180.1220.1261.0000.1270.2760.0570.3340.0640.0820.1260.0360.0600.0870.066
lead_time0.0890.1680.088-0.0060.1100.1090.1390.061-0.0070.0280.0250.0750.1830.2270.1130.1520.2390.1271.0000.1750.091-0.1800.0870.0710.1790.0460.3940.247-0.075
market_segment0.0000.0070.2630.0420.1110.1040.1330.1320.0000.0230.1050.3110.0920.2750.6650.2190.2280.2760.1751.0000.1800.0910.0420.1060.1720.1480.0750.0830.203
meal0.0000.0000.1860.0500.1020.1010.1260.0970.0000.0180.0300.1260.0630.0690.0630.2860.1410.0570.0910.1801.0000.0150.0880.0290.1060.0740.0900.0760.054
previous_bookings_not_canceled-0.134-0.200-0.1230.009-0.070-0.0700.0450.011-0.0160.0250.0000.036-0.0330.0120.1030.0560.0640.334-0.1800.0910.0151.0000.1200.0260.0450.008-0.111-0.0900.023
previous_cancellations-0.082-0.022-0.022-0.0240.0520.0480.0580.019-0.010-0.0300.0000.006-0.0270.0630.0370.0350.0570.0640.0870.0420.0880.1201.0000.0000.0410.0130.0050.004-0.036
required_car_parking_spaces0.0000.0000.0810.0100.0300.0300.0420.0960.0230.0180.0310.0570.0420.0680.0790.2030.2720.0820.0710.1060.0290.0260.0001.0000.1920.0800.0380.0440.060
reservation_status0.0000.0060.1210.0220.1320.1420.1520.1880.0000.0400.0390.1410.0470.2970.1450.3961.0000.1260.1790.1720.1060.0450.0410.1921.0000.0660.0520.0350.156
reserved_room_type0.0000.0000.1280.0160.0600.0560.1410.7320.0000.0160.3860.1220.0380.1270.1190.3180.0910.0360.0460.1480.0740.0080.0130.0800.0661.0000.0760.0670.089
stays_in_week_nights0.1460.1450.213-0.0180.0260.0230.0950.0710.0310.0950.0300.149-0.0050.0750.0410.2690.0680.0600.3940.0750.090-0.1110.0050.0380.0520.0761.0000.4280.101
stays_in_weekend_nights0.0940.1300.215-0.0160.0200.0190.0720.0620.0260.0580.0330.132-0.1000.0650.0690.1760.0430.0870.2470.0830.076-0.0900.0040.0440.0350.0670.4281.0000.103
total_of_special_requests0.1300.1380.2460.0050.0180.0150.1220.0850.1000.0550.0490.115-0.1380.1550.0770.2220.2180.066-0.0750.2030.0540.023-0.0360.0600.1560.0890.1010.1031.000

Missing values

2025-09-21T12:53:57.434360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-21T12:53:58.002742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-21T12:53:58.886123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_date
0Resort Hotel03422015727100200BBPRTDirectDirect000CC3No Deposit0.000.0Transient0.00.00.0Check-Out2015-07-012015-07-01
1Resort Hotel07372015727100200BBPRTDirectDirect000CC4No Deposit0.000.0Transient0.00.00.0Check-Out2015-07-012015-07-01
2Resort Hotel072015727101100BBGBRDirectDirect000AC0No Deposit0.000.0Transient75.00.00.0Check-Out2015-07-022015-07-01
3Resort Hotel0132015727101100BBGBRCorporateCorporate000AA0No Deposit304.000.0Transient75.00.00.0Check-Out2015-07-022015-07-01
4Resort Hotel0142015727102200BBGBROnline TATA/TO000AA0No Deposit240.000.0Transient98.00.01.0Check-Out2015-07-032015-07-01
5Resort Hotel0142015727102200BBGBROnline TATA/TO000AA0No Deposit240.000.0Transient98.00.01.0Check-Out2015-07-032015-07-01
6Resort Hotel002015727102200BBPRTDirectDirect000CC0No Deposit0.000.0Transient107.00.00.0Check-Out2015-07-032015-07-01
7Resort Hotel092015727102200FBPRTDirectDirect000CC0No Deposit303.000.0Transient103.00.01.0Check-Out2015-07-032015-07-01
8Resort Hotel1852015727103200BBPRTOnline TATA/TO000AA0No Deposit240.000.0Transient82.00.01.0Canceled2015-05-062015-07-01
9Resort Hotel1752015727103200HBPRTOffline TA/TOTA/TO000DD0No Deposit15.000.0Transient105.50.00.0Canceled2015-04-222015-07-01
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_date
58884City Hotel1605201610431712200BBPRTGroupsTA/TO000AA0Non Refund1.000.0Transient60.000.00.0Canceled2016-09-202016-10-17
58885City Hotel1605201610431712200BBPRTGroupsTA/TO000AA0Non Refund1.000.0Transient60.000.00.0Canceled2016-09-202016-10-17
58886City Hotel1605201610431712200BBPRTGroupsTA/TO000AA0Non Refund1.000.0Transient60.000.00.0Canceled2016-09-202016-10-17
58887City Hotel1605201610431712200BBPRTGroupsTA/TO000AA0Non Refund1.000.0Transient60.000.00.0Canceled2016-09-202016-10-17
58888City Hotel1605201610431712200BBPRTGroupsTA/TO000AA0Non Refund1.000.0Transient60.000.00.0Canceled2016-09-202016-10-17
58890Resort Hotel0320164161110100BBPRTOnline TATA/TO000AA0No Deposit240.000.0Transient-Party56.000.01.0Check-Out2016-04-122016-04-11
58891Resort Hotel11582016520822200BBPRTDirectDirect000FF2No Deposit250.000.0Transient83.050.01.0Canceled2016-01-212016-05-08
58892City Hotel1182016832622200BBESPOnline TATA/TO000AA0No Deposit9.000.0Transient151.000.02.0Canceled2016-07-282016-08-06
58893Resort Hotel138320161041613200BBPRTGroupsTA/TO000AA0No Deposit315.000.0Transient-Party48.000.00.0Canceled2016-03-042016-10-06
58894City Hotel11852016728504200BBDEUOnline TATA/TO000AA0No Deposit9.000.0Transient90.950.01.0Canceled2016-05-312016-07-05

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_date# duplicates
1312City Hotel118820166251502100BBPRTOffline TA/TOTA/TO000AA0Non Refund119.039.0Transient130.00.00.0Canceled2016-01-182016-06-1592
740City Hotel137201610421303200BBPRTOffline TA/TOTA/TO000AA0No Deposit56.00.0Transient-Party105.00.00.0Canceled2016-09-062016-10-1379
1224City Hotel115820165222402100BBPRTGroupsTA/TO000AA0Non Refund37.031.0Transient130.00.00.0Canceled2016-01-182016-05-2479
744City Hotel13920158331402200HBPRTOffline TA/TOTA/TO000AA0No Deposit6.00.0Transient-Party101.50.00.0Canceled2015-07-062015-08-1469
893City Hotel17120166251403100BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00.0Transient120.00.00.0Canceled2016-04-272016-06-1469
554City Hotel1120162102821100BBPRTOffline TA/TOTA/TO000AA0No Deposit134.00.0Transient-Party60.00.00.0Canceled2016-02-272016-02-2864
966City Hotel18720159392523200BBPRTGroupsTA/TO000AA0Non Refund1.00.0Transient170.00.00.0Canceled2015-09-092015-09-2559
818City Hotel1562016624801200BBPRTOffline TA/TOCorporate000AA0No Deposit191.00.0Transient-Party120.00.00.0Canceled2016-06-022016-06-0858
910City Hotel17420159381802200HBPRTOffline TA/TOTA/TO000AA0Non Refund6.00.0Transient-Party101.50.00.0Canceled2015-07-062015-09-1854
1058City Hotel11052016415601200BBPRTOffline TA/TOTA/TO000AA0Non Refund12.00.0Transient75.00.00.0Canceled2016-01-182016-04-0653